Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs) can provide Internet of Things(Io T) devices with global computing services. Sometimes, the network state information is uncertain or unknown. To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper. The problem of minimizing the average sum task completion delay of all Io T devices over all time periods is formulated. We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed, which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.